Paper Info
Title
Effect of database drift on network topology and enrichment analyses: a case study for RegulonDB.
Abstract
RegulonDB is a database storing the biological information behind the transcriptional regulatory network (TRN) of the bacterium Escherichia coli. It is one of the key bioinformatics resources for Systems Biology investigations of bacterial gene regulation. Like most biological databases, the content drifts with time, both due to the accumulation of new information and due to refinements in the underlying biological concepts. Conclusions based on previous database versions may no longer hold. Here, we study the change of some topological properties of the TRN of E. coli, as provided by RegulonDB across 16 versions, as well as a simple index, digital control strength, quantifying the match between gene expression profiles and the transcriptional regulatory networks. While many of network characteristics change dramatically across the different versions, the digital control strength remains rather robust and in tune with previous results for this index. Our study shows that: (i) results derived from network topology should, when possible, be studied across a range of database versions, before detailed biological conclusions are derived, and (ii) resorting to simple indices, when interpreting high-throughput data from a network perspective, may help achieving a robustness of the findings against variation of the underlying biological information.
Year
DOI
Venue
2016
10.1093/database/baw003
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Field
DocType
Volume
Data mining,Computer science,Biological database,Systems biology,Network topology,Robustness (computer science),Bioinformatics,Gene regulatory network,Database
Journal
2016
ISSN
Citations 
PageRank 
1758-0463
0
0.34
References 
Authors
13
3
Name
Order
Citations
PageRank
Moritz Emanuel Beber162.07
Georgi Muskhelishvili2182.09
Marc-Thorsten Hütt39013.65